POSTER: Privacy-preserving Indoor Localization

Upcoming WiFi-based localization systems for indoor environments face a conflict of privacy interests: Server-side localization violates location privacy of the users, while localization on the user's device forces the localization provider to disclose the details of the system, e.g., sophisticated classification models. We show how Secure Two-Party Computation can be used to reconcile privacy interests in a state-of-the-art localization system. Our approach provides strong privacy guarantees for all involved parties, while achieving room-level localization accuracy at reasonable overheads.Comment: Poster Session of the 7th ACM Conference on Security & Privacy in Wireless and Mobile Networks (WiSec'14

Developing Digital Privacy: Children’s Moral Judgments Concerning Mobile GPS Devices

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141945/1/cdev12826_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141945/2/cdev12826.pd

Complying with Data Handling Requirements in Cloud Storage Systems

In past years, cloud storage systems saw an enormous rise in usage. However, despite their popularity and importance as underlying infrastructure for more complex cloud services, today's cloud storage systems do not account for compliance with regulatory, organizational, or contractual data handling requirements by design. Since legislation increasingly responds to rising data protection and privacy concerns, complying with data handling requirements becomes a crucial property for cloud storage systems. We present PRADA, a practical approach to account for compliance with data handling requirements in key-value based cloud storage systems. To achieve this goal, PRADA introduces a transparent data handling layer, which empowers clients to request specific data handling requirements and enables operators of cloud storage systems to comply with them. We implement PRADA on top of the distributed database Cassandra and show in our evaluation that complying with data handling requirements in cloud storage systems is practical in real-world cloud deployments as used for microblogging, data sharing in the Internet of Things, and distributed email storage.Comment: 14 pages, 11 figures; revised manuscript, accepted for publication in IEEE Transactions on Cloud Computin

BLOOM: BLoom filter based oblivious outsourced matchings

Whole genome sequencing has become fast, accurate, and cheap, paving the way towards the large-scale collection and processing of human genome data. Unfortunately, this dawning genome era does not only promise tremendous advances in biomedical research but also causes unprecedented privacy risks for the many. Handling storage and processing of large genome datasets through cloud services greatly aggravates these concerns. Current research efforts thus investigate the use of strong cryptographic methods and protocols to implement privacy-preserving genomic computations

Knowledge is at the Edge! How to Search in Distributed Machine Learning Models

With the advent of the Internet of Things and Industry 4.0 an enormous amount of data is produced at the edge of the network. Due to a lack of computing power, this data is currently send to the cloud where centralized machine learning models are trained to derive higher level knowledge. With the recent development of specialized machine learning hardware for mobile devices, a new era of distributed learning is about to begin that raises a new research question: How can we search in distributed machine learning models? Machine learning at the edge of the network has many benefits, such as low-latency inference and increased privacy. Such distributed machine learning models can also learn personalized for a human user, a specific context, or application scenario. As training data stays on the devices, control over possibly sensitive data is preserved as it is not shared with a third party. This new form of distributed learning leads to the partitioning of knowledge between many devices which makes access difficult. In this paper we tackle the problem of finding specific knowledge by forwarding a search request (query) to a device that can answer it best. To that end, we use a entropy based quality metric that takes the context of a query and the learning quality of a device into account. We show that our forwarding strategy can achieve over 95% accuracy in a urban mobility scenario where we use data from 30 000 people commuting in the city of Trento, Italy.Comment: Published in CoopIS 201

Формування конкурентних переваг підприємства в умовах зовнішньоекономічної діяльності

Abstract Background Whole genome sequencing has become fast, accurate, and cheap, paving the way towards the large-scale collection and processing of human genome data. Unfortunately, this dawning genome era does not only promise tremendous advances in biomedical research but also causes unprecedented privacy risks for the many. Handling storage and processing of large genome datasets through cloud services greatly aggravates these concerns. Current research efforts thus investigate the use of strong cryptographic methods and protocols to implement privacy-preserving genomic computations. Methods We propose Fhe-Bloom and Phe-Bloom, two efficient approaches for genetic disease testing using homomorphically encrypted Bloom filters. Both approaches allow the data owner to securely outsource storage and computation to an untrusted cloud. Fhe-Bloom is fully secure in the semi-honest model while Phe-Bloom slightly relaxes security guarantees in a trade-off for highly improved performance. Results We implement and evaluate both approaches on a large dataset of up to 50 patient genomes each with up to 1000000 variations (single nucleotide polymorphisms). For both implementations, overheads scale linearly in the number of patients and variations, while Phe-Bloom is faster by at least three orders of magnitude. For example, testing disease susceptibility of 50 patients with 100000 variations requires only a total of 308.31 s (σ=8.73 s) with our first approach and a mere 0.07 s (σ=0.00 s) with the second. We additionally discuss security guarantees of both approaches and their limitations as well as possible extensions towards more complex query types, e.g., fuzzy or range queries. Conclusions Both approaches handle practical problem sizes efficiently and are easily parallelized to scale with the elastic resources available in the cloud. The fully homomorphic scheme, Fhe-Bloom, realizes a comprehensive outsourcing to the cloud, while the partially homomorphic scheme, Phe-Bloom, trades a slight relaxation of security guarantees against performance improvements by at least three orders of magnitude

Längsschnittstudie zum Verlauf motorischer Fähigkeiten von Grundschulkindern in Abhängigkeit auffälliger motorischer Leistungen der Fein- und Grobmotorik

Theoretischer Hintergrund: Die motorische Leistungsfähigkeit (MLF) spielt eine zentrale Rolle in der Kindesentwicklung. Über den Verlauf der MLF über die Grundschulzeit in Abhängigkeit auffälliger motorischer Leistungen im Vorschulalter liegen nur wenige Befunde vor. Fragestellung: Liegen unterschiedliche Entwicklungsverläufe derMLF bei Kindern mitmotorisch auffälligen Leistungen in der Fein- und Grobmotorik vor? Methode: Innerhalb einer Längsschnittstudie wurden die motorischen Dimensionen Kraft, Ausdauer, Schnelligkeit, Koordination und Beweglichkeit von Grundschulkindern (N=424) jährlich untersucht und mittels Varianzanalyse mit Messwiederholung geprüft. Ergebnisse: Kinder, die vor Schuleintritt grob- oder feinmotorische Auffälligkeiten (9-15 %) aufwiesen, blieben in ihrer motorischen Entwicklung deutlich hinter motorisch unauffälligen Kindern zurück. Diskussion und Schlussfolgerung: In der Folge können sich erhebliche Einschränkungen für die Alltagsmotorik und das Erlernen komplexer Bewegungen ergeben. Um gleiche motorische Startbedingungen für die betroffenen Kinder herzustellen, stellt die Erweiterung der bewegungsbezogenen Förderung der MLF vor Schulbeginn einen notwendigen Ansatz dar.Theoretical Background: Motor performance is an important matter in the health-related development of children, particularly for perception and for establishing a personal and material environment using physical activity. Developmental coordination disorders in preschoolaged children may relate to lower levels of fine and gross motor development. Short-term longitudinal studies revealed that preschoolers with motor deficits fall behind in their overall motor performance during the 1st and 2nd grades of primary school. Moreover, the years at primary school are a meaningful stage in life for children because of its rapid progression in motor-learning capability. Objective: Regarding children in primary school, little is known about the effects of developmental coordination disorders on the grade-related progression of basic motor abilities (i.e., flexibility, strength, endurance, speed of movement, coordination). This study analyzes the motor performance development of children over the period of primary school. Method: Using a longitudinal study design (KOMPASS-2 Study), we examined motor ability development in a sample of N = 424 primary school-aged children (n = 218 girls, 51.4%). To assess levels of motor abilities, we used the German Motor Test (DMT 6-18). Based on standard screening for school entry, children were separated into two groups based on the status of their fine and gross motor development. Changes inmotor ability levels were analyzed via robust repeated measures analyses of variance (rmANOVA) regarding developmental group effects, school timeframe effects, and interaction effects. Results: 9% (n = 39) of the children were classified with a gross motor disorder, and 15% (n = 62) of the children were classified with a fine motor disorder. The statistical analyses with rmANOVA demonstrated that children with developmental coordination disorders regarding gross or fine motor developmental status showed lower motor-ability levels on all test tasks compared to children without disorders. Particularly gross-motor disordered children achieved significantly lower motor-ability levels regarding coordination under time constraint (jumping sideways) and coordination during dynamic precision tasks (backward balancing). However, motor-ability levels increased significantly over time for both developmental status groups. Discussion and conclusion: Children with developmental coordination disorders may experience substantial restrictions to meeting daily physical activities and motor learning of complex movements. To create equal motor developmental conditions for children just starting school, it is necessary to promote physical activity in general. Interventions should regard a set of coordination tasks that require children’s attention and speed during movement. Measuring the motor abilities of primary-school-aged children once a year should be an integral part of communal health monitoring